Papers by Vaidehi Patil
Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages (2022.acl-long)
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| Challenge: | Pre-trained multilingual models have shown great potential for zero-shot cross-lingual transfer to low web-resource languages (LRLs). |
| Approach: | They propose a vocabulary generation algorithm which enhances lexical overlap across related languages by generating a token that increases the representation of LRLs. |
| Outcome: | The proposed approach improves cross-lingual transfer accuracy without reducing HRL representation and accuracy. |
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study (2021.acl-long)
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Yash Khemchandani, Sarvesh Mehtani, Vaidehi Patil, Abhijeet Awasthi, Partha Talukdar, Sunita Sarawagi
| Challenge: | Recent research in multilingual language models (LMs) has demonstrated their ability to effectively handle multiple languages in a single model. |
| Approach: | They propose to exploit relatedness among languages in a language family to overcome corpora limitations of LRLs. |
| Outcome: | The proposed model exploits relatedness among languages in a language family to overcome corpora limitations for low web-resource languages. |
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks (2026.findings-acl)
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| Challenge: | Survey aims to identify challenges of multimodal unlearning for vision, language, audio and video . retraining after deletion requests or policy updates is often impractical, survey finds . |
| Approach: | They propose to enable selective removal across modalities while retaining overall utility. |
| Outcome: | This study compares models with existing models to identify weaknesses and improves performance. |
REFINESUMM: Self-Refining MLLM for Generating a Multimodal Summarization Dataset (2024.acl-long)
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| Challenge: | generating accurate and faithful multimodal summaries is challenging due to lack of appropriate multimodal datasets . large language models excel at synthesizing key information from diverse sources, but lack of adequate multimodal data sets for fine-tuning . |
| Approach: | They propose a dataset specifically designed for image-text multimodal summarization . they generate summaries from Wikipedia sections and corresponding images and evaluate them . |
| Outcome: | The proposed dataset improves summary quality by training a critic model on human annotations and using its predictions to remove low-quality summaries. |
Debiasing Multimodal Models via Causal Information Minimization (2023.findings-emnlp)
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| Challenge: | Existing methods for debiasing multimodal models use approximate heuristics to represent the biases, such as shallow features from early stages of training or unimodal features for multimodal tasks like VQA, which may not be accurate. |
| Approach: | They propose a method that leverages causally-motivated information minimization to learn the confounder representations of a causal graph for multimodal data. |
| Outcome: | The proposed method improves out-of-distribution performance on multiple multimodal datasets without sacrificing in-distance performance. |